Fariborz Bahrami; Aslan Egdernezhad
Abstract
Background and Purpose: Due to the complexities in the nature of ground water systems, it sounds like a demanding job to model either the time or the location of ground water. However, artificial neural networks have a high capability to model both complicated and non-linear models. Besides, Geostatistic ...
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Background and Purpose: Due to the complexities in the nature of ground water systems, it sounds like a demanding job to model either the time or the location of ground water. However, artificial neural networks have a high capability to model both complicated and non-linear models. Besides, Geostatistic Methods are, to a good extent, accurate in modelling ground water.Material and Methods: The aim of this study is to simulate groundwater quality parameters (SAR, TDS and EC) of Dezful Andimeshk plain using ANN-PSO and geostatistical models. For this purpose, information from 61 observation wells in Dezful-Andimeshk plain has been used. Neural network model inputs including qualitative parameters SO42- ، pH ، HCO32-، Na+، Mg2+، Ca2+، TDS، SAR and EC were considered.Results: The results of simulation with intelligent model showed that the highest accuracy of ANN-PSO model in simulation is related to EC, SAR and TDS parameters, respectively. The results of interpolation by geostatistical method showed that the highest accuracy of Kriging model in simulation is related to EC, TDS and SAR parameters, respectively. The general results obtained from the simulation of groundwater quality parameters showed that the ANN-PSO model is more accurate in simulating the groundwater quality parameters of the plain in Andimeshk than the Kriging model. So that the value of R2 for simulating SAR, TDS and EC parameters using ANN-PSO model in the test phase is 0.92, 0.918 and 0.955 respectively and using kriging model is 0.902. 0.915 and 0.931 were estimated.Conclusion: The results of this study also showed that the combination of intelligent models with optimization algorithms is used as a useful tool to simulate groundwater quality parameters.
Jeyran Askari; Aslan Egdernezhad
Abstract
Background and Aim: Groundwater is one of the most important water resources on earth, and groundwater level and groundwater salinity studies are very important to protect and plan the water resources, especially in the arid and semiarid areas, such as Iran. Groundwater quantitative and qualitative testing ...
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Background and Aim: Groundwater is one of the most important water resources on earth, and groundwater level and groundwater salinity studies are very important to protect and plan the water resources, especially in the arid and semiarid areas, such as Iran. Groundwater quantitative and qualitative testing is time-consuming and costly. Therefore, using the models to simulate the quantity and quality of groundwater has become common.Materials and Methods: In recent decades, the artificial intelligence models were tested for the simulation of aquifers in terms of the complex and nonlinear properties of groundwater systems. The present study stimulated the groundwater level and groundwater salinity parameters of Dezful-Andimeshk plain using ANN and ANN + GA models, and finally compared their results with measured data. The data collected for input to two models include meteorological data and groundwater quality parameters gathered from 2011 to 2018.Results: The results showed that the optimal model is to simulate ANN + GA (Artificial Neural Network + Genetic Algorithm) groundwater level with sigmoid tangent stimulus function, and the optimal model is to simulate ANN + GA groundwater salinity with sigmoid logarithm stimulus function. MAE and RMSE statistics have the minimum and has maximum value for the model (In test phase, for the groundwater level RMSE=7.47, MAE=9.5 and R2=0.979 and for the groundwater salinity RMSE=6.81, MAE=7.74, and R2=0.99).Conclusion: Therefore, optimizing the artificial neural network model using a genetic algorithm is very useful, effective and reduces errors and saves time and money.
Seyed Ali Mohammadi Nezhad; Aslan Egder Nezhad
Abstract
The present study stimulated the groundwater quality parameters of Zeidoun plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN and ANN-GA models and in the end compare their results with measured data. The input data for TDS quality parameter ...
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The present study stimulated the groundwater quality parameters of Zeidoun plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN and ANN-GA models and in the end compare their results with measured data. The input data for TDS quality parameter consist of Na, EC, Ca, Mg, SO4 and SAR, for SAR including the Na, TDS, Hco3, Ca and Mg and quality parameter of EC contains Ca, Mg, SO4, Na and SAR, gathered from 2011 to 2018.The results showed that in ANN and ANN-GA models, the highest accuracy of SAR simulation in the model with sigmoid tangent function, in EC simulator model, the highest accuracy in ANN and ANN-GA models, respectively, related to logarithm stimulus functions. Sigmoid and tangent is sigmoid. Also in ANN and ANN-GA models, the highest accuracy of TDS simulation was obtained in the model with sigmoid tangent stimulus and sigmoid logarithm, respectively. so that the MAE and RMSE statistics have the minimum and R^2 has the maximum value for the model. In general, according to the obtained results, the accuracy of ANN-GA model is higher than ANN model, to simulate the groundwater quality parameters of Zeidoun plain. Therefore, the use of artificial neural network model along with genetic algorithm is a good tool to simulate high quality groundwater quality parameters, without the need for measurement and laboratory work, which requires high time and cost.
Ali Reza Karimiyan; Aslan Egdernezhad
Abstract
Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect ...
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Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect water resources. Most of these models require input parameters that are hardly available or their measurements are time-consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by the human brain are a better choice. Materials and Methods: The present study simulated the groundwater level and salinity in Ramhormoz plain using ANN and ANN+PSO models and compared their results with the measured data. The data collected as inputs of the two models included minimum temperature, maximum temperature, average temperature, wind speed at 2 m altitude, minimum relative humidity, maximum relative humidity, average relative humidity, and sunshine hours gathered from 2011 to 2017. Results: The results indicated that the highest prediction accuracy of groundwater level and salinity was achieved by the ANN-PSO model with the logarithm sigmoid activation function. Thus, the MAE and RMSE statistics had the minimum and R^2 had the maximum value for the model. Conclusion: Considering the high efficiency of artificial neural network models with Particle Swarm Optimization algorithm training, it can be used to make managerial decisions, ensure the results of monitoring, and reduce costs. Keywords: Groundwater Level; Simulation; Groundwater Salinity; Artificial Neural Networks Model
Eslam Nazari; Aslan Egdernezhad; Reza Jalilzadeh Yengejeh
Abstract
Background and Aim: Monitoring water quality is so important so as to decide about using them. So, this research was conducted to evaluate Khuzitan’s river water quality. Materials and Methods: The rivers were studied consist of Dez, Karkheh, Maroon, Karoon and Zohreh. Data collecting was applied ...
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Background and Aim: Monitoring water quality is so important so as to decide about using them. So, this research was conducted to evaluate Khuzitan’s river water quality. Materials and Methods: The rivers were studied consist of Dez, Karkheh, Maroon, Karoon and Zohreh. Data collecting was applied during 2018 for each river from specified stations. So, water quality standard of Iran, WHO and Canadian council of ministers of the environment, and Shoeller diagram and Wilcox diagram were used. In addition, IRWQIsc and NSFWQI standards were used to categorize river water quality. Results: The results showed that Dez water was industrially corrosive, while other rivers had sedimentary water for industrial use. The water quality of Dez was better than other rivers in Khuzestan province, but this river also had high magnesium, hardness and chlorine based on the Shoeller diagram. The quality of this river was better for agricultural purposes rather than the others. Karun River was moderately better than other rivers, and water quality is better upstream than downstream. According to IRWQIsc index, the water quality variations of Dez, Karkheh, Karoon, Maroon and Zohreh were 71-83, 41-52, 39-55, 33-41 and 25-32, respectively. The results of NSFWQI index for Dez, Karkheh, Karoon, Maroon and Zohreh rivers showed that the values of these rivers varied between 65-77, 55-70, 58-68, 52-60 and 36-48, respectively. Conclusion: Thus Dez River was in relatively good condition. Karoon and Karkheh rivers were in moderate condition and other rivers were in relatively poor condition. According to all indices, water quality of Zohreh River was in poor condition and Dez River was in good condition. Other rivers had medium quality.